Decoding the heterogeneity of Alzheimer's disease diagnosis and progression using multilayer networks

Abstract

Alzheimer's disease (AD) is a multifactorial and heterogeneous disorder, which makes early detection a challenge. Studies have attempted to combine biomarkers to improve AD detection and predict progression. However, most of the existing work reports results in parallel or compares normalized findings but does not analyze data simultaneously. We tested a multi-dimensional network framework, applied to 490 subjects (cognitively normal [CN] = 147; mild cognitive impairment [MCI] = 287; AD = 56) from ADNI, to create a single model capable of capturing the heterogeneity and progression of AD. First, we constructed subject similarity networks for structural magnetic resonance imaging, amyloid-β positron emission tomography, cerebrospinal fluid, cognition, and genetics data and then applied multilayer community detection to find groups with shared similarities across modalities. Individuals were also followed-up longitudinally, with AD subjects having, on average, 4.5 years of follow-up. Our findings show that multilayer community detection allows for accurate identification of present and future AD (≈90%) and is also able to identify cases that were misdiagnosed clinically. From all MCI participants who developed AD or reverted to CN, the multilayer model correctly identified 90.8% and 88.5% of cases respectively. We observed similar subtypes across the full sample and when examining multimodal data from subjects with no AD pathology (i.e., amyloid negative). Finally, these results were also validated using an independent testing set. In summary, the multilayer framework is successful in detecting AD and provides unique insight into the heterogeneity of the disease by identifying subtypes that share similar multidisciplinary profiles of neurological, cognitive, pathological, and genetics information.

Fig. 1. Overview of the multilayer network framework used in the study and resulting communities.

a The first column displays each individual modality included in the multilayer network model (subjects x features), while the second column shows the corresponding similarity networks (subjects x subjects). In the third column, the multilayer network is displayed, with each diamond representing a layer, solid lines representing intralayer interactions, and dotted lines representing interlayer interactions. Nodes are connected across and within layers. The last column exemplifies multilayer community detection, where two communities are identified based on communalities in the data. b Multilayer network communities and distribution for each diagnosis group across the entire sample, (c) in the amyloid negative subsample, and (d) in the amyloid positive subsample. The matrices represent the similarity between subjects across features quantified using pair-wise correlations.

Fig. 2. Community 1 and 2 divided by change in diagnosis across the full sample.

Percentages correspond to the proportion of individuals within each category that belong to community 1 or 2.

Fig. 3. Amyloid and tau load.

a PET amyloid load (av45), (b) CSF tau, and (c) CSF pTau for community 1 and 2 by diagnosis group.

Fig. 4. Differences across the brain in.

a PET amyloid load and (b) volume. The full sample compares all subjects in community 1 (i.e., CN dominant) to all subjects in community 2 (i.e., AD dominant), whereas CN and AD mismatches compare only CN (N = 123 vs. 27) or AD (N = 12 vs. 123) cases between communities. U-values were scaled (divided by maximum U) for ease of interpretation. Results were Bonferroni corrected unless otherwise stated (all except for volumetric findings in CN mismatched cases, which were FDR corrected instead). Masks were weighted using results of t-tests or Whitney–Mann U tests. These were based on ROIs from the Desikan-Killiany Atlas, and included the amygdala, nucleus accumbens, hippocampus, pallidum, thalamus, caudate, and ventral diencephalon (ventral DC).